Energy Efficient and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

Abstract

The main concern of clustering approaches for mobile wireless sensor networks (WSNs) is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach the need of a powerful mechanism to safely elect a cluster head remains a challenging task in many research works that take into account the mobility of the network. The approach based on the computing of the weight of each node in the network is one of the proposed techniques to deal with this problem. In this paper, we propose an energy efficient and safe weighted clustering algorithm (ES-WCA) for mobile WSNs using a combination of five metrics. Among these metrics lies the behavioral level metric which promotes a safe choice of a cluster head in the sense where this last one will never be a malicious node. Moreover, the highlight of our work is summarized in a comprehensive strategy for monitoring the network, in order to detect and remove the malicious nodes. We use simulation study to demonstrate the performance of the proposed algorithm.

A Virtual Laboratory to Practice Mobile Wireless Sensor Networks: A Case Study on Energy Efficient and Safe Weighted Clustering Algorithm

“…Fig. 10 depicts the average number of clusters that are formed, with respect to the total number of nodes in the network [27]. The communication range used in this experiment is 200 m. As shown in Fig.…”

“…The result of the remaining amount of energy per node for each protocol of AODV and DSDV is presented in Fig. 13, such as R is equal to 35 m [27]. As shown in the above-mentioned figure, the remaining energy for each node in the AODV protocol is greater than that in the DSDV protocol, such as AODV, which consumes 22.74% less than DSDV.…”

Balanced and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

“…In our earlier work [9], we insisted in Mobility ( ), connectivity ( ), residual energy ( ) and distance of node ( ) to its neighbors. In this paper, we focus our study on behavior level metric.…”

“…The clustering concept, that means grouping nodes which are close to each other, has been studied largely in ad-hoc networks [2,3,4,5,6,7,8] and recently in WSNs [9,10,11,12,13] where the purpose in general is to reduce useful energy consumption and routing overhead, however, cluster-heads must be selected carefully and diligently. Recent research studies recognize that organizing mobile WSNs, in the sense defined above, into clusters by using a clustering mechanism is a challenging task [9,10]. This is due to the fact that cluster heads (CHs) carry out extra work, and consequently consume more energy compared with cluster members (CMs) during the network operations and this will lead to untimely death causing network partition and therefore failure in communication link.…”

“…Indeed, a CH can be selected by computing quality of nodes, which may depend on several metrics: connectivity degree, mobility, residual energy and distance of a node from its neighbors. Significant improvement in performance of this quality can be achieved by combining these metrics [2,3,8,9,13,14].…”